AI Summary & Key Takeaways
AI InsightWe demonstrate the proposed approach in a well-known design of experiment by comparing it with existing methods.
📌 3 Key Takeaways
Abstract
Especially in a world where industrial development is reinforced by globalization tendencies, competitive companies know that satisfying customers' needs and running a successful operation requires a process that is reliable, predictable and robust. Therefore, many of quality improvement techniques focus on reducing process variation in line with the “loss to society” concept. The upside-down normal loss function is a weighted loss function that has the ability to evaluate losses with a more reasonable risk assessment. In this study, we introduce a fuzzy modelling approach based on expected upside-down normal loss function where the mean and standard deviation responses are fitted by response surface models. The proposed method aims to identify a set of operating conditions to maximize the degree of satisfaction with respect to the expected loss. Additionally, the proposed approach provides a more informative and realistic approach for comparing competing sets of conditions depending upon how much better or worse a process is. We demonstrate the proposed approach in a well-known design of experiment by comparing it with existing methods.